Object (e.g., table, index, etc) histograms are an important aspect of a database. Histograms are used by a query optimizer of a database management system (DBMS) to estimate the cost of alternative data access plans (for a given database query) for accessing the data stored in a data table contained in the database, and to select the most efficient data access plan for that data table. Therefore the task of recomputing histograms for a data table is an important task to be executed by the DBMS so that the most cost effective access plan may be selected for database queries to be received in the future.
There are known methods for directing the DBMS to compute histograms for a data table, such as for example the Chi-Square test, the Kolmogorov-Smirnov test, etc. However, these known methods have drawbacks. The DBMS may use the Kolmogorov-Smirnov to examine histograms which may indicate a measure of a “maximum estimation error” that the query optimizer may incur for a particular histogram. Reliance on the Kolmogorov-Smirnov test may cause the DBMS to experience difficulty in determining just when it may be desirable to recompute histograms for each column of the data table.
Accordingly, a solution is desired that addresses, at least in part, these shortcomings.